Chapter 1, What Is Analytics Engineering?, traces the history of analytics engineering and its surge in popularity. Dive into its why and what to understand role responsibilities thoroughly.

Chapter 2, The Modern Data Stack, explores the modern data stack, demystifying SQL and cloud impact. Witness the industry’s shift to purpose-built tools for contemporary data management.

Chapter 3, Data Ingestion, explores fundamental techniques, common issues, and strategies for moving data between systems. We break down data ingestion into eight steps and elaborate on common considerations for data quality and scalability.

Chapter 4, Data Warehousing, delves into the core concepts and history of data warehouses. You will gain insights into the evolution of data storage solutions and the impact of cloud technologies on this space.

Chapter 5, Data Modeling, details the proactive design process of establishing relationships between data within information systems. This critical process can reduce costs, enhance computational speed, and elevate user experience.

Chapter 6, Transforming Data, unravels the shift from ETL to ELT pipeline paradigms, the importance of data cleaning and transformation, reusability in query processes, and optimizing SQL queries for modularity.

Chapter 7, Serving Data, discusses presenting data to end users. You will gain insights into exposing data through different means, understanding data as a product, and examining the motivations and challenges associated with achieving self-service analytics in companies.

Chapter 8, Hands-On Analytics Engineering, also describes tools such as Airbyte Cloud for managed ingestion, Google BigQuery for warehousing, dbt Cloud for transformations, and Tableau for visualization.

Chapter 9, Data Quality and Observability, helps you ensure data quality and establish observability in analytics processes. Delving into strategies and tools, this chapter equips you with the skills to maintain data integrity and transparency.

Chapter 10, Writing Code in a Team, focuses on collaborative coding practices within a team setting. With an emphasis on best practices, version control, and effective communication, this chapter focuses on teamwork and efficiency in analytics engineering projects.

Chapter 11, Automating Workflows, concludes the DataOps section by exploring the implementation of continuous workflows. You will be introduced to practices that streamline analytics workflows, optimizing efficiency and productivity.

Chapter 12, Driving Business Adoption, highlights the critical process of collecting and interpreting business requirements. You will be guided through the steps to understand and align analytics initiatives with the unique needs and objectives of the business.

Chapter 13, Data Governance, delves into the principles and practices of data governance. You will gain insights into establishing robust data governance frameworks, ensuring the reliability of organizational data, and aligning analytics strategies with overarching business goals.

Chapter 14, Epilogue, summarizes the learning from this book and gives you extra tips to take your analytics engineering career even further.